{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IDEA\\Documents\\experimentos\\pits\\lucia2022\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\IDEA\\\\Documents\\\\experimentos\\\\pits\\\\lucia2022'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Modify working directory accordingly. This step is not necessary because the directory will be specified below. A portion of the code below is adapted from Deep Learning with Python, by Francois Chollet, 2017, Manning Publications.\n",
    "\n",
    "%cd \"C:\\\\Users\\\\IDEA\\\\Documents\\\\experimentos\\\\pits\\\\lucia2022\"\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.4.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import keras\n",
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MODULE 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, shutil\n",
    "original_dir= \"C:\\\\Users\\\\IDEA\\\\Documents\\\\experimentos\\\\pits\\\\lucia2022\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The directory where we will\n",
    "# store our smaller dataset\n",
    "base_dir = \"C:\\\\Users\\\\IDEA\\\\Documents\\\\experimentos\\\\pits\\\\lucia2022\\\\conv\"\n",
    "os.mkdir(base_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directories for our training,validation/test splits\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "os.mkdir(train_dir)\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "os.mkdir(validation_dir)\n",
    "\n",
    "\n",
    "# Directory with our training lion pictures\n",
    "train_lions_dir = os.path.join(train_dir, 'LP')\n",
    "os.mkdir(train_lions_dir)\n",
    "\n",
    "# Directory with our training hyena pictures\n",
    "train_hyenas_dir = os.path.join(train_dir, 'HP1')\n",
    "os.mkdir(train_hyenas_dir)\n",
    "\n",
    "# Directory with our validation lion pictures\n",
    "validation_lions_dir = os.path.join(validation_dir, 'LP')\n",
    "os.mkdir(validation_lions_dir)\n",
    "\n",
    "# Directory with our validation hyena pictures\n",
    "validation_hyenas_dir = os.path.join(validation_dir, 'HP1')\n",
    "os.mkdir(validation_hyenas_dir)\n",
    "\n",
    "\n",
    "\n",
    "# Copy first 20 CM images to train_lions_dir\n",
    "fnames = ['LP.{}.bmp'.format(i) for i in range(25)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dir, fname)\n",
    "    dst = os.path.join(train_lions_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "# Copy next 10 CM images to validation_lions_dir\n",
    "fnames = ['LP.{}.bmp'.format(i) for i in range(25, 34)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dir, fname)\n",
    "    dst = os.path.join(validation_lions_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "\n",
    "    \n",
    "# Copy first 20 TM images to train_hyenas_dir\n",
    "fnames = ['HP1.{}.bmp'.format(i) for i in range(30)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dir, fname)\n",
    "    dst = os.path.join(train_hyenas_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "# Copy next 10 TM images to validation_hyenas_dir\n",
    "fnames = ['HP1.{}.bmp'.format(i) for i in range(30, 44)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dir, fname)\n",
    "    dst = os.path.join(validation_hyenas_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CREATE X TRAIN X TEST Y LABELS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 55 images belonging to 2 classes.\n",
      "Found 23 images belonging to 2 classes.\n",
      "(55, 150, 200, 3) train samples\n",
      "(23, 150, 200, 3) test samples\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "datagenTrain=train_datagen.flow_from_directory(\n",
    "        # This is the target directory\n",
    "        train_dir,\n",
    "        # All images will be resized to 40x160\n",
    "        target_size=(150, 200),\n",
    "        batch_size=55,\n",
    "        # Since we use binary_crossentropy loss, we need binary labels\n",
    "        class_mode='binary')\n",
    "datagenTest=test_datagen.flow_from_directory(\n",
    "        # This is the target directory\n",
    "        validation_dir,\n",
    "        # All images will be resized to 40x160\n",
    "        target_size=(150, 200),\n",
    "        batch_size=23,\n",
    "        # Since we use binary_crossentropy loss, we need binary labels\n",
    "        class_mode='binary',\n",
    "        shuffle=False)\n",
    "x_train, y_train = next(datagenTrain)\n",
    "x_test, y_test = next(datagenTest)\n",
    "print(x_train.shape, 'train samples')\n",
    "print(x_test.shape, 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Select pre-processing standardization according to transfer model\n",
    "\n",
    "from keras.applications.densenet import preprocess_input#DENSENET201\n",
    "from keras.applications.resnet50 import preprocess_input#RESNET50\n",
    "from keras.applications.inception_resnet_v2 import preprocess_input #InceptionResNetV2 \n",
    "from keras.applications.nasnet import preprocess_input #NASNetLarge\n",
    "from keras.applications.inception_v3 import preprocess_input #InceptionV3\n",
    "from keras.applications.vgg19 import preprocess_input #VGG19\n",
    "from keras.applications.vgg16 import preprocess_input #VGG16\n",
    "from keras.applications.imagenet_utils import decode_predictions, preprocess_input#for efficient Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 55 images belonging to 2 classes.\n",
      "Found 23 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "#No AUGMENTATION\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.applications.resnet50 import preprocess_input #AQUÍ COPIAR DE ARRIBA SEGÚN MODELO\n",
    "from keras.applications.resnet50 import preprocess_input#RESNET50\n",
    "\n",
    "# All images will be rescaled by 1./255\n",
    "#train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "#test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "#for DENSENET\n",
    "train_datagen = ImageDataGenerator(\n",
    "    preprocessing_function= \\\n",
    "    keras.applications.resnet50.preprocess_input)#OJO!!!\n",
    "test_datagen = ImageDataGenerator(\n",
    "    preprocessing_function= \\\n",
    "    keras.applications.resnet50.preprocess_input)#OJO!!!\n",
    "\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        # This is the target directory\n",
    "        train_dir,\n",
    "        # All images will be resized to 40x160\n",
    "        target_size=(150, 200),\n",
    "        batch_size=20,\n",
    "        # Since we use binary_crossentropy loss, we need binary labels\n",
    "        class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(150, 200),\n",
    "        batch_size=10,\n",
    "        class_mode='binary', \n",
    "        shuffle=False)#or True for more randomization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 55 images belonging to 2 classes.\n",
      "Found 23 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "#WITH AUGMENTATION\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from keras.applications.imagenet_utils import decode_predictions, preprocess_input#for efficient Net#modify according to model selected\n",
    "#from keras.applications.resnet50 import preprocess_input#RESNET50\n",
    "\n",
    "#Let's train our network using data augmentation:\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    preprocessing_function= \\\n",
    "    keras.applications.imagenet_utils.preprocess_input)#OJO!!!!\n",
    "\n",
    "# Note that the validation data should not be augmented!\n",
    "test_datagen = ImageDataGenerator(preprocessing_function= \\\n",
    "    keras.applications.imagenet_utils.preprocess_input)#OJO!!!\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        # This is the target directory\n",
    "        train_dir,\n",
    "        # All images will be resized to 150x150\n",
    "        target_size=(150, 200),\n",
    "        batch_size=20,\n",
    "        # Since we use binary_crossentropy loss, we need binary labels\n",
    "        class_mode='binary')#put \"categorical\" si son más de 2 categorías\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(150, 200),\n",
    "        batch_size=10,\n",
    "        class_mode='binary',#use \"categorical\" for non-binomials\n",
    "        shuffle=False)#or True for more randomization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MODULE 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#MODELS: select one and move to the next module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transfer learning fpr VGG19\n",
    "import sys\n",
    "from matplotlib import pyplot\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.optimizers import SGD\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# define cnn model\n",
    "def define_model():\n",
    "\t# load model\n",
    "\tmodel = VGG19(include_top=False, input_shape=(150, 200, 3))\n",
    "\t# mark loaded layers as not trainable\n",
    "\tfor layer in model.layers:\n",
    "\t\tlayer.trainable = False\n",
    "\t# add new classifier layers\n",
    "\tflat1 = Flatten()(model.layers[-1].output)\n",
    "\tclass1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)\n",
    "\toutput = Dense(1, activation='sigmoid')(class1)\n",
    "\t# define new model\n",
    "\tmodel = Model(inputs=model.inputs, outputs=output)\n",
    "\t# compile model\n",
    "\topt = SGD(lr=0.001, momentum=0.9)\n",
    "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\treturn model\n",
    "\n",
    "# define model\n",
    "model = define_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  for transfer learning DenseNet201\n",
    "import sys\n",
    "from matplotlib import pyplot\n",
    "from keras.applications.densenet import DenseNet201\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.optimizers import SGD\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.applications.densenet import DenseNet201, preprocess_input\n",
    "from keras.applications.densenet import preprocess_input\n",
    "\n",
    "# define cnn model\n",
    "def define_model():\n",
    "\t# load model\n",
    "\tmodel = DenseNet201(include_top=False, input_shape=(150, 200, 3))\n",
    "\t# mark loaded layers as not trainable\n",
    "\tfor layer in model.layers:\n",
    "\t\tlayer.trainable = False\n",
    "\t# add new classifier layers\n",
    "\tflat1 = Flatten()(model.layers[-1].output)\n",
    "\tclass1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)\n",
    "\toutput = Dense(1, activation='sigmoid')(class1)\n",
    "\t# define new model\n",
    "\tmodel = Model(inputs=model.inputs, outputs=output)\n",
    "\t# compile model\n",
    "\topt = SGD(lr=0.001, momentum=0.9)\n",
    "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\treturn model\n",
    "\n",
    "# define model\n",
    "model = define_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for transfer learning RESNET50\n",
    "import sys\n",
    "from matplotlib import pyplot\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.optimizers import SGD\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# define cnn model\n",
    "def define_model():\n",
    "\t# load model\n",
    "\tmodel = ResNet50(include_top=False, input_shape=(150, 200, 3))\n",
    "\t# mark loaded layers as not trainable\n",
    "\tfor layer in model.layers:\n",
    "\t\tlayer.trainable = False\n",
    "\t# add new classifier layers\n",
    "\tflat1 = Flatten()(model.layers[-1].output)\n",
    "\tclass1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)\n",
    "\toutput = Dense(1, activation='sigmoid')(class1)\n",
    "\t# define new model\n",
    "\tmodel = Model(inputs=model.inputs, outputs=output)\n",
    "\t# compile model\n",
    "\topt = SGD(lr=0.001, momentum=0.9)\n",
    "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\treturn model\n",
    "\n",
    "# define model\n",
    "model = define_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FUNCIONA!!!!! for transfer learning EfficientNetB7\n",
    "import sys\n",
    "from matplotlib import pyplot\n",
    "from tensorflow.keras.applications import EfficientNetB7\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.optimizers import SGD\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# define cnn model\n",
    "def define_model():\n",
    "\t# load model\n",
    "\tmodel = EfficientNetB7(include_top=False, input_shape=(150, 200, 3))\n",
    "\t# mark loaded layers as not trainable\n",
    "\tfor layer in model.layers:\n",
    "\t\tlayer.trainable = False\n",
    "\t# add new classifier layers\n",
    "\tflat1 = Flatten()(model.layers[-1].output)\n",
    "\tclass1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)\n",
    "\toutput = Dense(1, activation='sigmoid')(class1)\n",
    "\t# define new model\n",
    "\tmodel = Model(inputs=model.inputs, outputs=output)\n",
    "\t# compile model\n",
    "\topt = SGD(lr=0.001, momentum=0.9)\n",
    "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\treturn model\n",
    "\n",
    "# define model\n",
    "model = define_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  for transfer learning InceptionResNetV2\n",
    "import sys\n",
    "from matplotlib import pyplot\n",
    "from keras.applications.inception_resnet_v2 import InceptionResNetV2 \n",
    "from keras.models import Model\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.optimizers import SGD\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# define cnn model\n",
    "def define_model():\n",
    "\t# load model\n",
    "\tmodel = InceptionResNetV2 (include_top=False, input_shape=(150, 200, 3))\n",
    "\t# mark loaded layers as not trainable\n",
    "\tfor layer in model.layers:\n",
    "\t\tlayer.trainable = False\n",
    "\t# add new classifier layers\n",
    "\tflat1 = Flatten()(model.layers[-1].output)\n",
    "\tclass1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)\n",
    "\toutput = Dense(1, activation='sigmoid')(class1)\n",
    "\t# define new model\n",
    "\tmodel = Model(inputs=model.inputs, outputs=output)\n",
    "\t# compile model\n",
    "\topt = SGD(lr=0.001, momentum=0.9)\n",
    "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\treturn model\n",
    "\n",
    "# define model\n",
    "model = define_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MODULE 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#run selected model next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "3/3 [==============================] - 34s 4s/step - loss: 0.7072 - accuracy: 0.6214 - val_loss: 0.4460 - val_accuracy: 0.8261\n",
      "Epoch 2/100\n",
      "3/3 [==============================] - 2s 700ms/step - loss: 0.4092 - accuracy: 0.8680 - val_loss: 0.4364 - val_accuracy: 0.8261\n",
      "Epoch 3/100\n",
      "3/3 [==============================] - 2s 790ms/step - loss: 0.1902 - accuracy: 0.9422 - val_loss: 0.1515 - val_accuracy: 0.9565\n",
      "Epoch 4/100\n",
      "3/3 [==============================] - 2s 673ms/step - loss: 0.1516 - accuracy: 0.9568 - val_loss: 0.1834 - val_accuracy: 0.9130\n",
      "Epoch 5/100\n",
      "3/3 [==============================] - 2s 681ms/step - loss: 0.1273 - accuracy: 0.9565 - val_loss: 0.1701 - val_accuracy: 0.9130\n",
      "Epoch 6/100\n",
      "3/3 [==============================] - 2s 823ms/step - loss: 0.0461 - accuracy: 0.9909 - val_loss: 0.0806 - val_accuracy: 1.0000\n",
      "Epoch 7/100\n",
      "3/3 [==============================] - 2s 688ms/step - loss: 0.1028 - accuracy: 0.9818 - val_loss: 0.0844 - val_accuracy: 0.9565\n",
      "Epoch 8/100\n",
      "3/3 [==============================] - 2s 775ms/step - loss: 0.0533 - accuracy: 0.9747 - val_loss: 0.0796 - val_accuracy: 0.9565\n",
      "Epoch 9/100\n",
      "3/3 [==============================] - 2s 822ms/step - loss: 0.1439 - accuracy: 0.9180 - val_loss: 0.2089 - val_accuracy: 0.8696\n",
      "Epoch 10/100\n",
      "3/3 [==============================] - 2s 698ms/step - loss: 0.1176 - accuracy: 0.9713 - val_loss: 0.1555 - val_accuracy: 0.9565\n",
      "Epoch 11/100\n",
      "3/3 [==============================] - 2s 682ms/step - loss: 0.0390 - accuracy: 0.9722 - val_loss: 0.0715 - val_accuracy: 1.0000\n",
      "Epoch 12/100\n",
      "3/3 [==============================] - 2s 675ms/step - loss: 0.0862 - accuracy: 0.9722 - val_loss: 0.1149 - val_accuracy: 0.9130\n",
      "Epoch 13/100\n",
      "3/3 [==============================] - 2s 732ms/step - loss: 0.0664 - accuracy: 0.9818 - val_loss: 0.0737 - val_accuracy: 1.0000\n",
      "Epoch 14/100\n",
      "3/3 [==============================] - 2s 796ms/step - loss: 0.0445 - accuracy: 0.9909 - val_loss: 0.2288 - val_accuracy: 0.8696\n",
      "Epoch 15/100\n",
      "3/3 [==============================] - 2s 702ms/step - loss: 0.0979 - accuracy: 0.9636 - val_loss: 0.1705 - val_accuracy: 0.9130\n",
      "Epoch 16/100\n",
      "3/3 [==============================] - 2s 665ms/step - loss: 0.0250 - accuracy: 1.0000 - val_loss: 0.1619 - val_accuracy: 0.8696\n",
      "Epoch 17/100\n",
      "3/3 [==============================] - 2s 720ms/step - loss: 0.0922 - accuracy: 0.9838 - val_loss: 0.0657 - val_accuracy: 1.0000\n",
      "Epoch 18/100\n",
      "3/3 [==============================] - 2s 729ms/step - loss: 0.0971 - accuracy: 0.9713 - val_loss: 0.4331 - val_accuracy: 0.8696\n",
      "Epoch 19/100\n",
      "3/3 [==============================] - 2s 799ms/step - loss: 0.1100 - accuracy: 0.9474 - val_loss: 0.1742 - val_accuracy: 0.8696\n",
      "Epoch 20/100\n",
      "3/3 [==============================] - 2s 674ms/step - loss: 0.0772 - accuracy: 0.9622 - val_loss: 0.6591 - val_accuracy: 0.7391\n",
      "Epoch 21/100\n",
      "3/3 [==============================] - 2s 729ms/step - loss: 0.1784 - accuracy: 0.9172 - val_loss: 0.3040 - val_accuracy: 0.8696\n",
      "Epoch 22/100\n",
      "3/3 [==============================] - 2s 643ms/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.0811 - val_accuracy: 0.9565\n",
      "Epoch 23/100\n",
      "3/3 [==============================] - 2s 689ms/step - loss: 0.0627 - accuracy: 0.9818 - val_loss: 0.2289 - val_accuracy: 0.8696\n",
      "Epoch 24/100\n",
      "3/3 [==============================] - 2s 689ms/step - loss: 0.0286 - accuracy: 1.0000 - val_loss: 0.0873 - val_accuracy: 0.9565\n",
      "Epoch 25/100\n",
      "3/3 [==============================] - 2s 689ms/step - loss: 0.0601 - accuracy: 0.9722 - val_loss: 0.0808 - val_accuracy: 1.0000\n",
      "Epoch 26/100\n",
      "3/3 [==============================] - 2s 705ms/step - loss: 0.0233 - accuracy: 1.0000 - val_loss: 0.0717 - val_accuracy: 1.0000\n",
      "Epoch 27/100\n",
      "3/3 [==============================] - 2s 674ms/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9565\n",
      "Epoch 28/100\n",
      "3/3 [==============================] - 2s 697ms/step - loss: 0.0357 - accuracy: 0.9847 - val_loss: 0.1991 - val_accuracy: 0.8696\n",
      "Epoch 29/100\n",
      "3/3 [==============================] - 2s 702ms/step - loss: 0.0357 - accuracy: 0.9909 - val_loss: 0.2124 - val_accuracy: 0.8696\n",
      "Epoch 30/100\n",
      "3/3 [==============================] - 2s 704ms/step - loss: 0.0796 - accuracy: 0.9693 - val_loss: 0.0580 - val_accuracy: 1.0000\n",
      "Epoch 31/100\n",
      "3/3 [==============================] - 2s 823ms/step - loss: 0.1330 - accuracy: 0.9509 - val_loss: 0.1066 - val_accuracy: 0.9565\n",
      "Epoch 32/100\n",
      "3/3 [==============================] - 2s 674ms/step - loss: 0.0382 - accuracy: 0.9756 - val_loss: 0.0920 - val_accuracy: 0.9565\n",
      "Epoch 33/100\n",
      "3/3 [==============================] - 2s 662ms/step - loss: 0.0191 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9565\n",
      "Epoch 34/100\n",
      "3/3 [==============================] - 2s 687ms/step - loss: 0.0479 - accuracy: 0.9847 - val_loss: 0.0969 - val_accuracy: 0.9565\n",
      "Epoch 35/100\n",
      "3/3 [==============================] - 2s 748ms/step - loss: 0.1583 - accuracy: 0.9263 - val_loss: 0.3819 - val_accuracy: 0.8696\n",
      "Epoch 36/100\n",
      "3/3 [==============================] - 2s 743ms/step - loss: 0.0644 - accuracy: 0.9909 - val_loss: 0.5828 - val_accuracy: 0.8261\n",
      "Epoch 37/100\n",
      "3/3 [==============================] - 2s 661ms/step - loss: 0.2074 - accuracy: 0.9165 - val_loss: 0.4355 - val_accuracy: 0.8696\n",
      "Epoch 38/100\n",
      "3/3 [==============================] - 2s 674ms/step - loss: 0.2118 - accuracy: 0.9511 - val_loss: 0.7402 - val_accuracy: 0.8261\n",
      "Epoch 39/100\n",
      "3/3 [==============================] - 2s 726ms/step - loss: 0.1149 - accuracy: 0.9513 - val_loss: 0.1491 - val_accuracy: 0.8696\n",
      "Epoch 40/100\n",
      "3/3 [==============================] - 2s 704ms/step - loss: 0.2145 - accuracy: 0.9206 - val_loss: 0.3794 - val_accuracy: 0.8261\n",
      "Epoch 41/100\n",
      "3/3 [==============================] - 2s 697ms/step - loss: 0.0993 - accuracy: 0.9443 - val_loss: 0.2909 - val_accuracy: 0.8696\n",
      "Epoch 42/100\n",
      "3/3 [==============================] - 2s 792ms/step - loss: 0.0807 - accuracy: 0.9727 - val_loss: 0.7335 - val_accuracy: 0.8696\n",
      "Epoch 43/100\n",
      "3/3 [==============================] - 2s 740ms/step - loss: 0.0337 - accuracy: 0.9713 - val_loss: 0.3262 - val_accuracy: 0.8696\n",
      "Epoch 44/100\n",
      "3/3 [==============================] - 2s 746ms/step - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.0423 - val_accuracy: 1.0000\n",
      "Epoch 45/100\n",
      "3/3 [==============================] - 2s 651ms/step - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.0368 - val_accuracy: 1.0000\n",
      "Epoch 46/100\n",
      "3/3 [==============================] - 2s 778ms/step - loss: 0.0477 - accuracy: 0.9675 - val_loss: 0.0386 - val_accuracy: 1.0000\n",
      "Epoch 47/100\n",
      "3/3 [==============================] - 2s 676ms/step - loss: 0.0218 - accuracy: 0.9909 - val_loss: 0.1702 - val_accuracy: 0.9130\n",
      "Epoch 48/100\n",
      "3/3 [==============================] - 2s 712ms/step - loss: 0.0449 - accuracy: 0.9818 - val_loss: 0.1225 - val_accuracy: 0.9130\n",
      "Epoch 49/100\n",
      "3/3 [==============================] - 2s 691ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.0337 - val_accuracy: 1.0000\n",
      "Epoch 50/100\n",
      "3/3 [==============================] - 2s 712ms/step - loss: 0.0310 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9565\n",
      "Epoch 51/100\n",
      "3/3 [==============================] - 2s 689ms/step - loss: 0.0657 - accuracy: 0.9722 - val_loss: 0.0374 - val_accuracy: 1.0000\n",
      "Epoch 52/100\n",
      "3/3 [==============================] - 2s 716ms/step - loss: 0.0368 - accuracy: 0.9713 - val_loss: 0.0872 - val_accuracy: 0.9565\n",
      "Epoch 53/100\n",
      "3/3 [==============================] - 2s 787ms/step - loss: 0.0332 - accuracy: 0.9838 - val_loss: 0.1207 - val_accuracy: 0.9130\n",
      "Epoch 54/100\n",
      "3/3 [==============================] - 2s 690ms/step - loss: 0.0378 - accuracy: 0.9847 - val_loss: 0.0474 - val_accuracy: 0.9565\n",
      "Epoch 55/100\n",
      "3/3 [==============================] - 2s 732ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.0363 - val_accuracy: 1.0000\n",
      "Epoch 56/100\n",
      "3/3 [==============================] - 2s 682ms/step - loss: 0.0324 - accuracy: 0.9909 - val_loss: 0.0344 - val_accuracy: 1.0000\n",
      "Epoch 57/100\n",
      "3/3 [==============================] - 2s 686ms/step - loss: 0.0121 - accuracy: 1.0000 - val_loss: 0.0394 - val_accuracy: 1.0000\n",
      "Epoch 58/100\n",
      "3/3 [==============================] - 2s 689ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.0420 - val_accuracy: 1.0000\n",
      "Epoch 59/100\n",
      "3/3 [==============================] - 2s 811ms/step - loss: 0.0133 - accuracy: 1.0000 - val_loss: 0.0391 - val_accuracy: 1.0000\n",
      "Epoch 60/100\n",
      "3/3 [==============================] - 2s 675ms/step - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.0353 - val_accuracy: 1.0000\n",
      "Epoch 61/100\n",
      "3/3 [==============================] - 2s 714ms/step - loss: 0.0912 - accuracy: 0.9622 - val_loss: 0.0458 - val_accuracy: 1.0000\n",
      "Epoch 62/100\n",
      "3/3 [==============================] - 2s 781ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0428 - val_accuracy: 1.0000\n",
      "Epoch 63/100\n",
      "3/3 [==============================] - 2s 772ms/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.0410 - val_accuracy: 1.0000\n",
      "Epoch 64/100\n",
      "3/3 [==============================] - 2s 726ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0377 - val_accuracy: 1.0000\n",
      "Epoch 65/100\n",
      "3/3 [==============================] - 2s 720ms/step - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.0387 - val_accuracy: 1.0000\n",
      "Epoch 66/100\n",
      "3/3 [==============================] - 2s 710ms/step - loss: 0.0429 - accuracy: 0.9622 - val_loss: 0.0596 - val_accuracy: 0.9565\n",
      "Epoch 67/100\n",
      "3/3 [==============================] - 2s 684ms/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.1442 - val_accuracy: 0.9130\n",
      "Epoch 68/100\n",
      "3/3 [==============================] - 2s 713ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.2347 - val_accuracy: 0.9130\n",
      "Epoch 69/100\n",
      "3/3 [==============================] - 2s 688ms/step - loss: 0.0322 - accuracy: 0.9722 - val_loss: 0.3215 - val_accuracy: 0.8696\n",
      "Epoch 70/100\n",
      "3/3 [==============================] - 2s 772ms/step - loss: 0.0145 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9130\n",
      "Epoch 71/100\n",
      "3/3 [==============================] - 2s 681ms/step - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.1321 - val_accuracy: 0.9130\n",
      "Epoch 72/100\n",
      "3/3 [==============================] - 2s 794ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9130\n",
      "Epoch 73/100\n",
      "3/3 [==============================] - 2s 753ms/step - loss: 0.0111 - accuracy: 0.9909 - val_loss: 0.0897 - val_accuracy: 0.9130\n",
      "Epoch 74/100\n",
      "3/3 [==============================] - 2s 697ms/step - loss: 7.9822e-04 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9130\n",
      "Epoch 75/100\n",
      "3/3 [==============================] - 2s 688ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9130\n",
      "Epoch 76/100\n",
      "3/3 [==============================] - 2s 714ms/step - loss: 0.0239 - accuracy: 0.9838 - val_loss: 0.0652 - val_accuracy: 0.9565\n",
      "Epoch 77/100\n",
      "3/3 [==============================] - 2s 668ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.0373 - val_accuracy: 1.0000\n",
      "Epoch 78/100\n",
      "3/3 [==============================] - 2s 747ms/step - loss: 0.0361 - accuracy: 0.9838 - val_loss: 0.0688 - val_accuracy: 0.9565\n",
      "Epoch 79/100\n",
      "3/3 [==============================] - 2s 694ms/step - loss: 0.0403 - accuracy: 0.9909 - val_loss: 0.0474 - val_accuracy: 1.0000\n",
      "Epoch 80/100\n",
      "3/3 [==============================] - 2s 649ms/step - loss: 0.0242 - accuracy: 0.9722 - val_loss: 0.1397 - val_accuracy: 0.9130\n",
      "Epoch 81/100\n",
      "3/3 [==============================] - 2s 701ms/step - loss: 0.0216 - accuracy: 0.9909 - val_loss: 0.2268 - val_accuracy: 0.9130\n",
      "Epoch 82/100\n",
      "3/3 [==============================] - 2s 671ms/step - loss: 0.0300 - accuracy: 0.9756 - val_loss: 0.0486 - val_accuracy: 1.0000\n",
      "Epoch 83/100\n",
      "3/3 [==============================] - 2s 693ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0497 - val_accuracy: 1.0000\n",
      "Epoch 84/100\n",
      "3/3 [==============================] - 2s 741ms/step - loss: 0.0173 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9130\n",
      "Epoch 85/100\n",
      "3/3 [==============================] - 2s 706ms/step - loss: 0.0147 - accuracy: 1.0000 - val_loss: 0.0874 - val_accuracy: 0.9565\n",
      "Epoch 86/100\n",
      "3/3 [==============================] - 2s 762ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.0520 - val_accuracy: 1.0000\n",
      "Epoch 87/100\n",
      "3/3 [==============================] - 2s 672ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0384 - val_accuracy: 1.0000\n",
      "Epoch 88/100\n",
      "3/3 [==============================] - 2s 671ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.0387 - val_accuracy: 1.0000\n",
      "Epoch 89/100\n",
      "3/3 [==============================] - 2s 718ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.0389 - val_accuracy: 1.0000\n",
      "Epoch 90/100\n",
      "3/3 [==============================] - 2s 739ms/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.0411 - val_accuracy: 1.0000\n",
      "Epoch 91/100\n",
      "3/3 [==============================] - 2s 800ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.0413 - val_accuracy: 1.0000\n",
      "Epoch 92/100\n",
      "3/3 [==============================] - 2s 705ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.0390 - val_accuracy: 1.0000\n",
      "Epoch 93/100\n",
      "3/3 [==============================] - 2s 720ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0367 - val_accuracy: 1.0000\n",
      "Epoch 94/100\n",
      "3/3 [==============================] - 2s 690ms/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.0395 - val_accuracy: 1.0000\n",
      "Epoch 95/100\n",
      "3/3 [==============================] - 2s 694ms/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.0399 - val_accuracy: 1.0000\n",
      "Epoch 96/100\n",
      "3/3 [==============================] - 2s 711ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0383 - val_accuracy: 1.0000\n",
      "Epoch 97/100\n",
      "3/3 [==============================] - 2s 811ms/step - loss: 0.0439 - accuracy: 0.9671 - val_loss: 0.0538 - val_accuracy: 0.9565\n",
      "Epoch 98/100\n",
      "3/3 [==============================] - 2s 809ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.0792 - val_accuracy: 0.9565\n",
      "Epoch 99/100\n",
      "3/3 [==============================] - 2s 706ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9565\n",
      "Epoch 100/100\n",
      "3/3 [==============================] - 2s 695ms/step - loss: 0.0052 - accuracy: 1.0000 - val_loss: 0.0830 - val_accuracy: 0.9565\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=3,\n",
    "      epochs=100,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#guardar modelo\n",
    "model_json=model.to_json()\n",
    "with open(\"efficientnetB7_shuffle_false.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "#serializar los pesos\n",
    "model.save_weights(\"efficientnetB7_shuffle_true.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#WHEN SHUFFLE= FALSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IDEA\\Anaconda3\\envs\\gputest\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:1905: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 6s 169ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions=model.predict_generator(validation_generator, steps=len(validation_generator), verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "val_preds = np.round(predictions)# from probabilities to 0 or 1\n",
    "val_trues = validation_generator.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14  0]\n",
      " [ 1  8]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat= confusion_matrix (val_trues,val_preds)\n",
    "print(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      1.00      0.97        14\n",
      "           1       1.00      0.89      0.94         9\n",
      "\n",
      "    accuracy                           0.96        23\n",
      "   macro avg       0.97      0.94      0.95        23\n",
      "weighted avg       0.96      0.96      0.96        23\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(val_trues,val_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9444444444444444"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(val_trues,val_preds) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#when shuffle=true:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "[[14  0]\n",
      " [ 9  0]]\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      hyenas       0.61      1.00      0.76        14\n",
      "       lions       0.00      0.00      0.00         9\n",
      "\n",
      "    accuracy                           0.61        23\n",
      "   macro avg       0.30      0.50      0.38        23\n",
      "weighted avg       0.37      0.61      0.46        23\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IDEA\\Anaconda3\\envs\\gputest\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\IDEA\\Anaconda3\\envs\\gputest\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\IDEA\\Anaconda3\\envs\\gputest\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "nb_validation_samples=23\n",
    "batch_size=10\n",
    "\n",
    "Y_pred = model.predict_generator(validation_generator, nb_validation_samples // \n",
    "batch_size+1)\n",
    "y_pred = np.argmax(Y_pred, axis=1)\n",
    "\n",
    "print('Confusion Matrix')\n",
    "print(confusion_matrix(validation_generator.classes, y_pred))\n",
    "\n",
    "print('Classification Report')\n",
    "target_names = ['hyenas', 'lions']\n",
    "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#PLOT\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MODULE 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the code below is for ensemble learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "import keras.backend as K\n",
    "import numpy as np\n",
    "import cv2\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cargar modelo:\n",
    "from keras.models import model_from_json\n",
    "# cargar json y crear el modelo\n",
    "json_file = open('vgg19_shuffle_false.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "model1 = model_from_json(loaded_model_json)#change sequentially\n",
    "# cargar pesos al nuevo modelo\n",
    "model1.load_weights(\"vgg19_shuffle_false.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cargar modelo:\n",
    "from keras.models import model_from_json\n",
    "# cargar json y crear el modelo\n",
    "json_file = open('resnet_shuffle_false.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "model2 = model_from_json(loaded_model_json)#change sequentially\n",
    "# cargar pesos al nuevo modelo\n",
    "model2.load_weights(\"resnet_shuffle_false.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cargar modelo:\n",
    "from keras.models import model_from_json\n",
    "# cargar json y crear el modelo\n",
    "json_file = open('inceptionV2_shuffle_false.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "model3 = model_from_json(loaded_model_json)#change sequentially\n",
    "# cargar pesos al nuevo modelo\n",
    "model3.load_weights(\"inceptionV2_shuffle_false.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cargar modelo:\n",
    "from keras.models import model_from_json\n",
    "# cargar json y crear el modelo\n",
    "json_file = open('densenet_shuffle_false.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "model4 = model_from_json(loaded_model_json)#change sequentially\n",
    "# cargar pesos al nuevo modelo\n",
    "model4.load_weights(\"densenet_shuffle_false.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cargar modelo:\n",
    "from keras.models import model_from_json\n",
    "# cargar json y crear el modelo\n",
    "json_file = open('efficientnetB7_shuffle_false.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "model5 = model_from_json(loaded_model_json)#change sequentially\n",
    "# cargar pesos al nuevo modelo\n",
    "model5.load_weights(\"efficientnetB7_shuffle_false.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "members=[model1,model2,model3,model4,model5]#actualizar!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001CD286DD5E8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "Stacked Test Accuracy: 0.957\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from keras.models import load_model\n",
    "from keras.utils import to_categorical\n",
    "from numpy import dstack\n",
    "\n",
    " \n",
    "# create stacked model input dataset as outputs from the ensemble\n",
    "def stacked_dataset(members, inputX):\n",
    "\tstackX = None\n",
    "\tfor model in members:\n",
    "\t\t# make prediction\n",
    "\t\tyhat = model.predict(inputX, verbose=0)\n",
    "\t\t# stack predictions into [rows, members, probabilities]\n",
    "\t\tif stackX is None:\n",
    "\t\t\tstackX = yhat\n",
    "\t\telse:\n",
    "\t\t\tstackX = dstack((stackX, yhat))\n",
    "\t# flatten predictions to [rows, members x probabilities]\n",
    "\tstackX = stackX.reshape((stackX.shape[0], stackX.shape[1]*stackX.shape[2]))\n",
    "\treturn stackX\n",
    " \n",
    "# fit a model based on the outputs from the ensemble members\n",
    "def fit_stacked_model(members, inputX, inputy):\n",
    "\t# create dataset using ensemble\n",
    "\tstackedX = stacked_dataset(members, inputX)\n",
    "\t# fit standalone model\n",
    "\t#model = LogisticRegression()#\n",
    "\tmodel = RandomForestClassifier(n_estimators=100, max_depth=None,max_features='auto',min_samples_split=2, random_state=0)# n_estimators=n of trees\n",
    "\t#model = ExtraTreesClassifier()\n",
    "\tmodel = GradientBoostingClassifier()\n",
    "\tmodel.fit(stackedX, inputy)\n",
    "\treturn model\n",
    " \n",
    "# make a prediction with the stacked model\n",
    "def stacked_prediction(members, model, inputX):\n",
    "\t# create dataset using ensemble\n",
    "\tstackedX = stacked_dataset(members, inputX)\n",
    "\t# make a prediction\n",
    "\tyhat = model.predict(stackedX)\n",
    "\treturn yhat\n",
    " \n",
    "\n",
    "# fit stacked model using the ensemble\n",
    "model = fit_stacked_model(members, x_train, y_train)\n",
    "# evaluate model on test set\n",
    "yhat = stacked_prediction(members, model, x_test)\n",
    "acc = accuracy_score(y_test, yhat)\n",
    "print('Stacked Test Accuracy: %.3f' % acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cargar imagen para casificar\n",
    "\n",
    "from keras.preprocessing import image\n",
    "import keras.backend as K\n",
    "import numpy as np\n",
    "import cv2\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fosas de dientes de Marina (el modelo está en la carpeta \"pits\")\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "img_path = \"C:\\\\Users\\\\IDEA\\\\Documents\\\\experimentos\\\\pits\\\\marina\\\\PIT\\\\FLKN+410+2.bmp\"\n",
    "#img_path = \"C:\\\\Users\\\\IDEA\\\\Documents\\\\experimentos\\\\pits\\\\HP1.37.bmp\"\n",
    "\n",
    "img = image.load_img(img_path, target_size=(150, 200))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x /= 255\n",
    "#x = preprocess_input(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np\n",
    "imgplot = plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.]\n"
     ]
    }
   ],
   "source": [
    "xxx = stacked_prediction(members, model, x)\n",
    "print(xxx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.]\n"
     ]
    }
   ],
   "source": [
    "# make a prediction with the stacked model\n",
    "def stacked_pred(members, model, inputX):\n",
    "\t# create dataset using ensemble\n",
    "\tstackedX = stacked_dataset(members, inputX)\n",
    "\t# make a prediction\n",
    "\tyhat = model.predict(stackedX)\n",
    "\treturn yhat\n",
    "\n",
    "yyy = stacked_prediction(members, model, x)\n",
    "print(yyy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
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